Predicting Cloud Performance Using Real-time VM-level Metrics

Tian, J., Elhabbash, A. and Elkhatib, Y. (2022) Predicting Cloud Performance Using Real-time VM-level Metrics. In: 24th IEEE International Conference on High Performance Computing & Communications (HPCC-2022), Chengdu, China, 18-21 Dec 2022, pp. 1165-1172. ISBN 9798350319934 (doi: 10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00184)

Abstract

The vast range of cloud service offerings can easily overwhelm users and cause them to select ones that are unsuitable for their needs. As such, the literature has a number of proposals to predict application performance based on a history of executing a certain application or benchmark. However, this requires significant cost to pre-run the application on different service levels before identifying the most suitable one. We propose a machine learning model that enables a cloud user to select the optimal cloud service based on real-time execution without the need to do an exhaustive search. We develop and test this model using a popular benchmark suite on Microsoft Azure, a leading cloud provider. The key insight of this work is that fluctuations in rather than the absolute amount of utilization levels of CPU and memory can be strongly indicative of how well an application is executing.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Elkhatib, Dr Yehia
• Authors: Tian, J., Elhabbash, A., and Elkhatib, Y.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9798350319934
• Published Online: 28 March 2023
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in : 2022 IEEE 24th Int Conf on High Performance Computing & Communications
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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• Organisation

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
315521		ABC: Adaptive Brokerage for the Cloud	Yehia Elkhatib	Engineering and Physical Sciences Research Council (EPSRC)	EP/R010889/2	Computing Science